Yitong Zhu, Yuxuan Jiang, Guanxuan Jiang Hong Kong University of Science and Technology (Guangzhou), Bojing Hou, Peng Yuan Zhou, Ge Lin, Yuyang Wang
Multimodal Sentiment Analysis (MSA) aims to infer human sentiment from textual, acoustic, and visual signals. In real-world scenarios, however, multimodal inputs are often compromised by dynamic noise or modality missingness. Existing methods typically treat these imperfections as discrete cases or assume fixed corruption ratios, which limits their adaptability to continuously varying reliability conditions. To address this, we first introduce a Continuous Reliability Spectrum to unify missingness and quality degradation into a single framework. Building on this, we propose QA-MoE, a Quality-Aware Mixture-of-Experts framework that quantifies modality reliability via self-supervised aleatoric uncertainty. This mechanism explicitly guides expert routing, enabling the model to suppress error propagation from unreliable signals while preserving task-relevant information. Extensive experiments indicate that QA-MoE achieves competitive or state-of-the-art performance across diverse degradation scenarios and exhibits a promising One-Checkpoint-for-All property in practice.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Text-to-motion generation aims to synthesize realistic and semantically aligned 3D human motions from natural language descriptions. Existing diffusion-based methods often rely on isotropic latent priors and shallow cross-modal supervision, which lead to semantic entanglement, limited controllability, and poor interpretability. We propose HESP, a unified diffusion framework that hierarchically enhances semantic priors for disentangled text-driven motion generation. At its core, HESP introduces an Adaptive Gaussian Variational Autoencoder (AG-VAE) that structures the latent motion manifold into multiple semantically coherent submanifolds, enabling interpretable and controllable motion representations. To further bridge linguistic and kinematic semantics, we design a Dynamic Cross-Modal Memory (DCMM) module for adaptive semantic fusion and a Hierarchical Cross-Modal Attention (HCA) mechanism to capture multi-level text-motion correspondences. Extensive experiments on HumanML3D and KIT-ML demonstrate that HESP consistently outperforms state-of-the-art baselines such as SALAD, MoMask, and MDM, achieving improvement while maintaining higher diversity and physical plausibility. Moreover, the structured latent space of HESP provides interpretable clusters that reveal clear semantic boundaries among different motion categories.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Natural language provides valuable auxiliary information for enhancing visual object tracking. While existing vision-language tracking methods explicitly leverage linguistic descriptions to aid tracking, they suffer from two critical limitations: the inability to dynamically adapt descriptions to the moving target and changing context; and the strong dependency on language input may causes failure when text is unavailable. To address the issues, we design a simple yet effective plug-and-play module that leverages linguistic assistance implicitly, without requiring explicit language input. The proposed textual inversion module converts visual features from template and search regions into text tokens in the CLIP text embedding space. It effectively inverts visual representations into linguistic forms, integrating contextual information from the both template and search region. The linguistic cues are then injected into the visual feature space via a multi-layer semantic injection mechanism. The design enhances the completeness of cross-modal feature representations and the accuracy of inter-modal semantic alignment, thus enabling dynamically updated linguistic information guidance for general object tracking. Extensive experiments demonstrate the effectiveness of our proposed method. We integrate the proposed module into several advanced trackers and evaluate on both visual and vision-language tracking datasets, including MCITrack, DUTrack, and SeqTrack. By training only the newly introduced module and the corresponding decoder, the proposed approach achieves significant performance gains with minimal computational overhead. Code will be made publicly available.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表